Updates

Model and report changes

  1. The model now accounts for the ongoing immunisation programme, stratifying the population of people still susceptible to infection with the virus according to their immunisation status (unimmunised/1 dose/2 doses). We use data on the daily proportions of the population getting immunised to inform this splitting of the population, assuming that it takes three weeks for vaccine-derived immunity to develop. Vaccine efficacy is assumed against both infection and death and we have now revised our assumed values for vaccine efficacy to align with those estimated here. We have a changepoint in the vaccine efficacy on the 10th May, which marks a transition from alpha being the dominant variant, to delta.
  2. We have extended the use of serological sampling data to use samples taken beyond the first wave of the pandemic. The samples are those collected by NHS Blood and Transplant using the Roche-N assay, which measures the prevalence of infection-acquired antibodies in the population.
  3. The model also accounts for a different susceptibility to infection in each adult age group (no prior information is used); and for the under-15s, (using prior information from Viner et al, 2020, which estimates children to be less likely to acquire infection when in contact with an infectious individual).
  4. The model has the ability to incorporate estimates of community prevalence, by region and age group, from the Office of National Statistics COVID-19 Infection Survey (see Data Sources for details). These are included weekly since the outset of the Survey in May 2020 for the age groups >4 years to inform trends in incidence that are too recent to be captured by the data on deaths.
  5. The underlying probability of an unvaccinated individual dying following infection with SARS-CoV2 (the infection-fatality rate, IFR) is allowed to change gradually over the course of 30 days every (approximately) 100 days. This is designed to reflect fluctuations due to seasonal effects, demand on healthcare services or the emergence of new virus variants of differing severity.
  6. The ‘Epidemic summary’ only reports the current value for the IFR by age. To visualise how this has changed over time in our model, see the IFR tab in the ‘Infections and Deaths’ section of the report. The quantity that is now plotted under this tab is the probability of dying if infected, taking into account the impact of the immunisation programme - it is an average of a lower rate of death in vaccinated individuals and a higher rate among the unvaccinated.

Updated findings

  1. The estimated number of new daily infections on the 17th December across England is 57,600 (50,100–66,400, 95% credible interval). The daily infection rate is estimated to be 103 per 100k population per day nationally. The highest rate is now in London (GL) with 154 infections per 100K population followed by the East of England (EE) at 140. These rates correspond to 13,700 and 8,680 daily infections, respectively. There is then a drop to the South East (SE) (116), South West (SW)(109) and West Midlands (WM) (98). In the remaining regions rates are around 60 per 100K population. Note that a substantial proportion of these infections will be asymptomatic.
  2. The daily number of deaths has been declining, but it is now slowing to a plateau such that we forecast between 103 and 172 deaths per day by the 7th of January.
  3. This week we believe the national Rt to be very close to 1, with values estimated to be over 1 in the EE and GL, resulting in 92% and 77% probabilities of a growing epidemic, respectively. This probability is reasonably high in both the North West (NW) and WM (46% and 35% respectively). For everywhere else, the chance of Rt being above 1 is around 25% or less.
  4. The growth rate for England is 0.00 (-0.01– 0.01) per day. This means that, nationally, the number of infections is flat, corresponding to an Rt of around 1.0, slightly higher than the most recent estimate.
  5. Our estimates for the attack rate, that is the proportion of the regional populations who have ever been infected, have the North East (NE) at 51% and GL at 50%. WM, East Midlands (EM) and NW are all also above the national average with 46%, 43% and 43% respectively. The SE and SW continue to have the lowest attack rates at 34% and 33%. Note that the deaths data used are only very weakly informative on Rt over the last two weeks. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to significant revision.

Interpretation

The number of reported new positive cases (by date of specimen) has been increasing exponentially for the last two weeks, resulting from the rapid growth of the B.1.1.529 variant, known as omicron, particularly in GL. In contrast to the cases, admissions to hospitals have just started to rise after a period of decline, with, again, the increase particularly pronounced in GL. The prevalence of infection, as estimated by the ONS Coronavirus Infection Survey, is around 1.70% showing no real change from previous weeks. It is, as yet, too early for the omicron variant to have any impact on the numbers of deaths. As there is no clear signal from omicron in the data that we use (ONS prevalence, deaths, serological swabbing), it would appear that what is being estimated here is a summary for pandemic infection with the delta variant. This would, for example, explain a number of infections that is markedly lower than the daily reported number of SARS-CoV-2 cases.

Bearing this in mind, our estimates show a pandemic with Rt values estimated mainly very close to 1. This is reflected in infection incidence which has remained generally constant, apart from in GL and the EE where the number of infections has started to increase.

Plots of the IFR over time show that we are currently estimating a decreasing IFR in all age groups with the most steep fall in the younger age groups. Following this drop, the overall IFR is 0.24% (0.22%–0.25%), highest in the over-75s at 3.1% (2.9%–3.4%), similar to that estimated in our most recent publication.

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.
  3. Daily data on the numbers of people getting immunised by age-group and region. These data are derived from the National Immunisation Management Service (NIMS). These data includes all COVID-19 immunisations administered at hospital hubs, local immunisation service sites such as GP practices, and dedicated immunisation centres.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

Current IFR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.00 -0.01 0.01
East of England 0.01 0.00 0.02
East Midlands -0.03 -0.05 -0.01
London 0.00 -0.01 0.02
North East -0.03 -0.05 -0.01
North West 0.00 -0.02 0.02
South East -0.01 -0.02 0.01
South West 0.00 -0.02 0.01
West Midlands 0.00 -0.02 0.01
Yorkshire and The Humber -0.01 -0.03 0.01

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 1011.36 109.89 NA
East of England NA 185.04 NA
East Midlands 25.22 14.96 77.21
London NA 71.74 NA
North East 24.52 14.23 91.25
North West 658.97 27.72 NA
South East 137.13 36.38 NA
South West 140.26 33.66 NA
West Midlands 235.81 34.11 NA
Yorkshire and The Humber 57.51 20.07 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 117.84 NA
East of England 80.55 35.11 NA
East Midlands NA NA NA
London 149.28 39.91 NA
North East NA NA NA
North West NA 32.60 NA
South East NA 69.61 NA
South West NA 53.77 NA
West Midlands NA 59.69 NA
Yorkshire and The Humber NA 124.65 NA

Change in deaths incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.00 0.00 0.01
East of England 0.01 0.00 0.03
East Midlands -0.02 -0.03 -0.01
London 0.02 0.00 0.03
North East -0.02 -0.03 0.00
North West 0.00 -0.02 0.02
South East 0.00 -0.01 0.02
South West 0.00 -0.01 0.02
West Midlands 0.00 -0.01 0.01
Yorkshire and The Humber -0.01 -0.02 0.00

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 382.47 NA
East of England NA NA NA
East Midlands 37.21 24.05 126.40
London NA NA NA
North East 39.70 23.09 236.18
North West 8895.64 41.18 NA
South East NA 96.61 NA
South West NA 70.09 NA
West Midlands 3275.01 53.69 NA
Yorkshire and The Humber 64.60 28.73 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 234.68 83.38 NA
East of England 53.56 26.14 1664.71
East Midlands NA NA NA
London 45.75 24.68 212.29
North East NA NA NA
North West NA 36.05 NA
South East 147.51 37.74 NA
South West 347.54 40.85 NA
West Midlands NA 51.20 NA
Yorkshire and The Humber NA 197.24 NA
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Infections and deaths

The shaded areas show periods of national lockdown, the green lines the dates (once confirmed) of the steps in the roadmap in the UK Governement’s COVID-19 Response – Spring 2021, and the red line shows the date these results were produced (17 Dec).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Deaths incidence

By region

By age

Cumulative deaths

By region

By age

IFR

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

\(R_t\)

Copyright © MRC Biostatistics Unit, University of Cambridge